Unreleased breaking change: artifact loading

New source builds introduce a backward-incompatible safety default: recorder loads are restricted. If you reload saved models/datasets, resume online or delayed training, or use DDG-DA, verify the artifact writer and storage permissions before opting in with trusted=True (CLI: --trusted=True). Fresh in-memory training and supported data-only reads need no opt-in. HIST mappings and high-frequency cache paths also have migration requirements.

Follow the artifact loading migration guide before upgrading. Merging into main affects source installs before a new PyPI release; this change remains unreleased until the first tagged release containing it. That release's versioned upgrade notes should link to the same guide.

Upgrading configuration-driven workflows? See the configuration migration guide for the unreleased source changes: per-component trusted: true for local .py imports, restricted expressions, and explicit extension mappings. Package imports remain available. Match this guide to your checkout; PR source, main, and tagged/PyPI releases may differ.

:newspaper: What's NEW!   :sparkling_heart:

Recent released features

Introducing : LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D

We are excited to announce the release of RD-Agent📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.

RD-Agent is now available on GitHub, and we welcome your star🌟!

To learn more, please visit the RD-Agent repository. We have prepared several public demo videos for you:

• 📃Paper: R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
• 👾Code: https://github.com/microsoft/RD-Agent/

@misc{li2025rdagentquant,
    title={R\&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},
    author={Yuante Li and Xu Yang and Xiao Yang and Minrui Xu and Xisen Wang and Weiqing Liu and Jiang Bian},
    year={2025},
    eprint={2505.15155},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}

Features released before 2021 are not listed here.

Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning.

An increasing number of SOTA Quant research works/papers in diverse paradigms are being released in Qlib to collaboratively solve key challenges in quantitative investment. For example, 1) using supervised learning to mine the market's complex non-linear patterns from rich and heterogeneous financial data, 2) modeling the dynamic nature of the financial market using adaptive concept drift technology, and 3) using reinforcement learning to model continuous investment decisions and assist investors in optimizing their trading strategies.

It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. For more details, please refer to our paper "Qlib: An AI-oriented Quantitative Investment Platform".